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All Journal Sistemasi: Jurnal Sistem Informasi Share : Journal of Service Learning Jurnal Dedikasi Pendidikan Getsempena English Education Journal Jurnal Scientia International Journal of Engineering, Science and Information Technology Jurnal Ilmu Sosial dan Ilmu Politik Malikussaleh (JSPM) Studies in English Language and Education Innovative: Journal Of Social Science Research SAGA: Journal of Technology and Information Systems Applied Engineering, Innovation, and Technology Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Kurniawati Kurniawati
Universitas Malikussaleh
Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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IoT-Based LPG Gas Leak Monitoring System with Automatic Alarms Muhammad Hafizal; Safwandi Safwandi; Kurniawati Kurniawati; Muchlis Abd Muthalib; Said Fadlan Anshari
SISTEMASI Vol 15, No 6 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i6.6348

Abstract

Liquefied Petroleum Gas (LPG) leaks are one of the leading causes of fires in households and LPG distribution facilities. Limited public awareness and delays in detecting gas leaks can significantly increase the risk of property damage and endanger human safety. Therefore, this study aims to design and develop an Internet of Things (IoT)-based monitoring system for 3 kg LPG gas leaks that provides automatic, real-time early warning notifications. The proposed system employs an MQ-2 gas sensor to detect LPG concentration, a NodeMCU ESP8266 as the main controller and data communication module, and a buzzer, LED, and LCD as local warning indicators. A threshold-based method is implemented to classify safe and hazardous conditions according to predefined gas concentration limits. In addition, the system is integrated with remote notifications via Telegram, enabling users to receive alerts even when they are away from the monitored location. Experimental results demonstrate that the proposed system can accurately detect LPG gas leaks under various operating conditions and automatically send warning notifications whenever the gas concentration exceeds the predefined threshold. These findings indicate that the system is effective, responsive, and has strong potential to enhance user safety in the use of LPG.
HIGH SCHOOL TEACHERS’ PERCEPTION AND ATTITUDE TOWARDS WORLD ENGLISHES: STUDY CASES IN LHOKSEUMAWE CITY, INDONESIA Hanif Hanif; Kurniawati Kurniawati; Dini Rizki; Dewi Kumala Sari
Getsempena English Education Journal Vol. 12 No. 2 (2025)
Publisher : Universitas Bina Bangsa Getsempena

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46244/geej.v12i2.3491

Abstract

ABSTRACT This research explores the perceptions, attitudes, and teaching practices of senior high school English teachers in Lhokseumawe City, Aceh, Indonesia, regarding the concept of World Englishes (WE). As English continues to evolve as a global language, its use across diverse sociocultural contexts challenges traditional native-speaker norms—especially those based on British and American English. Despite this global shift, English education in Indonesia remains strongly influenced by native-speaker standards embedded within the national curriculum. Using a qualitative case study approach, the data were collected through semi-structured interviews, document analysis, and a literature review involving five English teachers from different schools in the area. The results indicate that although teachers recognize the existence of various English varieties, their comprehension of the theoretical and pedagogical principles underlying World Englishes is still limited. Most teachers continue to rely on inner circle varieties especially American and British English as the dominant teaching models, citing factors such as curriculum restrictions, insufficient exposure to global English varieties, and students’ relatively low proficiency levels. Consequently, the practical application of World Englishes in classroom instruction remains minimal. The study suggests that targeted professional development programs are essential to strengthen teachers’ sociolinguistic awareness and pedagogical skills in addressing English variation. Gradually incorporating non-native English varieties into classroom materials could foster more inclusive, realistic, and globally oriented English teaching practices in Indonesia. Keywords: World Englishes, teachers’ perceptions, English language teaching, pedagogical practice
Cataract Eye Disease Diagnosis Using the Random Forest Method Lilis Novita; Wahyu Fuadi; Kurniawati Kurniawati
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.777

Abstract

This study developed a machine learning-based classification model using the Random Forest algorithm to detect cataract risk based on 11 variables: age, gender, family history, lens opacity, visual acuity reduction, light sensitivity, color changes, double vision, intraocular pressure, slit-lamp results, and visual acuity. Feature importance analysis revealed that lens opacity and visual acuity variables contributed most significantly to cataract risk prediction, followed by intraocular pressure and visual acuity reduction. The system was designed using Google Colab for model training and Streamlit as an interactive interface, enabling real-time predictions with intuitive result visualization. After optimization using Grid Search, the model achieved an accuracy of 92.0%, precision of 95.0%, sensitivity of 90.0%, F1 Score of 92.4%, and specificity of 98.0%. This system is expected to serve as an effective supporting tool for medical professionals in the early diagnosis of cataracts.
Co-Authors Ananda Faridhatul Ulva Desvina Yulisda Dewi Kumala Sari Dewi Kumalasari Dini Rizki Hanif, Hanif Irfan Sahputra Lilis Novita M. Zawil Kiram Marina Marina Marina Marina Muchlis Abdul Muthalib Muhammad Fikry Muhammad Hafizal Munirul Ula Rasyimah, Rasyimah Safwandi Safwandi Said Fadlan Anshari Saiful Kiram Teuku Azhari Wahyu Fuadi